Ideology in the vision theater

Thanks to Kenneth Olwig for suggesting that ideology may be related to the argument from  the post  On the exterior homunculus fallacy . More precisely, Olwig points to the  following quote from The German Ideology by Karl Marx and Friedrich Engels:

If in all ideology men and their circumstances appear upside-down as in a camera obscura, this phenomenon arises just as much from their historical life-process as the inversion of objects on the retina does from their physical life-process. In direct contrast to German philosophy which descends from heaven to earth, here we ascend from earth to heaven. That is to say, we do not set out from what men say, imagine, conceive, nor from men as narrated, thought of, imagined, conceived, in order to arrive at men in the flesh. We set out from real, active men, and on the basis of their real life-process we demonstrate the development of the ideological reflexes and echoes of this life-process. The phantoms formed in the human brain are also, necessarily, sublimates of their material life-process, which is empirically verifiable and bound to material premises.

One of the first posts of this blog was The Cartesian Theater: philosophy of mind versus aerography, where I use the article “All that is landscape is melted into air: the `aerography’ of ethereal space”, Environment and Planning D: Society and Space 2011, volume 29, pages 519 – 532 by Olwig in order to argue that the Cartesian Theater notion of Dennett is showing only one half of the whole homunculus fallacy. Indeed, Dennett’s theater is a theater in a box, the invention of Inigo Jones, already designed around the king (or homunculus, in Dennett argument), using geometrical perspective for giving an appearance of reality to an artificial construct, the scenic space.

With any homunculus, I argue, comes also a scenic space, which has to be taken into account in any theory of mind, because it is as artificial, it leads to the same kind of fallacy as the homunculus. In the posts   Towards aerography, or how space is shaped to comply with the perceptions of the homunculus   and  Theatron as an eye  I further develop the subject by trying to see what becomes the homunculus fallacy if we use not the theater in a box, but the old greek theater instead (and apparently it seems that it stops to be a fallacy, as homunculi and designed scenic spaces melt into oblivion and the gnomon, the generator of self-similarity, comes to the attention). Finally, in   the post On the exterior homunculus fallacy  I argue that the original homunculus fallacy is not depending on the fact that the homunculus is inside or outside the brain, thus leading me to suppose that the neuroscientist which studies a fly’s vision system is an exterior homunculus with respect to the fly and the lab is the scenic space of this homunculus. It means that any explanation of the fly vision which makes use of arguments which are not physically embedded in the fly brain (like knowledge about the euclidean structure of the space) makes sense for the experimenter, but cannot be the real explanations, because the fly does not have a lab with a small homunculus inside the head.

Which brings me to the relation with ideology, which is more than a given point of view, is a theater in a box which invites the infected host to take the place of the homunculus, watch the show and make an opinion based on the privileged position it occupies. But the opinion can be only one, carefully designed by the author of the ideology, the scenographer.
The scenic space needs an Inigo Jones, Inigo Jones is the ignored dual of the homunculus-king. He does not use magic in order to organize the show for the king, but he adds meaning. In the case of an ideology (a word which has as root a greek word meaning “to see”, thanks again to Olwig for this) the added meaning is intentional, but in the case of a neuroscientist which experiments on the vision system of a fly (what a king) it is unintended, but still present, under the form of assumptions which lead the experimenter to an explanation of the fly vision which is different from what the fly does when seeing (likely  an evolving graph with neurons as nodes and synapses as edges, which modifies itself according to the input, without any exterior knowledge about the experimenter’s lab and techniques).

Spiral patterns in the full zipper graph (another post on the Chemical concrete machine)

A step for making something analogous to the Holliday junction, such that it is related to the Chemical concrete machine minimal formalism, is to make a full zipper. Let’s play with it.

I shall use two ingredients, which function only by using the graphic beta move, so I shall NOT use the FAN-IN move.

fullzipper_1

  • zippers, this time written with the conventions of the Chemical concrete machine minimal formalism.

fullzipper_2

If we put them together then we obtain graphs like this: (ignore for the moment the red and green curved  arrows, suppose they are not there)

fullzipper_3

Now, if we want the numbering 1-1′ 2-2′ 3-3′  to make sense, we may add arrows, the green and red ones. But mind you, we could also add graphs with one input and one output instead of each green or red arrow.  As usual, the colours are just a visual help, they mean nothing in the formalism.

Is just a play, but for the moment, since it’s holiday season, I like to think about the green and red arrows (in fact about their replacements by graphs with one input and one output) as being the letters A, C, G, T. Of course, it might mean nothing, let’s see, it’s just a vague hypothesis.

Left for play: draw a Holliday junction.

On the exterior homunculus fallacy

If we think about a homunculus outside the brain, the homunculus fallacy still functions.

This post continues the Vision theater series  part I, part II, part III, part IV, part V, part VI  and also links to the recent Another discussion about computing with space .

According to wikipedia:

The homunculus argument is a fallacy arising most commonly in the theory of vision. One may explain (human) vision by noting that light from the outside world forms an image on the retinas in the eyes and something (or someone) in the brain looks at these images as if they are images on a movie screen (this theory of vision is sometimes termed the theory of the Cartesian Theater: it is most associated, nowadays, with the psychologist David Marr). The question arises as to the nature of this internal viewer. The assumption here is that there is a ‘little man’ or ‘homunculus‘ inside the brain ‘looking at’ the movie.

The reason why this is a fallacy may be understood by asking how the homunculus ‘sees’ the internal movie. The obvious answer is that there is another homunculus inside the first homunculus’s ‘head’ or ‘brain’ looking at this ‘movie’. But how does this homunculus see the ‘outside world’? In order to answer this, we are forced to posit another homunculus inside this other homunculus’s head and so forth. In other words, we are in a situation of infinite regress. The problem with the homunculus argument is that it tries to account for a phenomenon in terms of the very phenomenon that it is supposed to explain.

Suppose instead that the homunculus is outside the brain. Why, for example think about the experimenter doing research on your vision. The fallacy functions as well, because now we have another homunculus (outside the brain) who looks at the movie screen (i.e. the measurements he performed on your visual system, in the medium controlled by him). “But how does this homunculus see the ‘outside world’?” Infinite regression again.

If you think that is outrageous, then let me give you an example. The exterior homunculus (experimenter) explains your vision by interpreting the controlled space  he put you in (the lab) and the measurements he performed. When he does this interpretation he relies on:

  • physical laws
  • geometrical assumptions
  • statistical assumptions

at least. Suppose that the experimenter says: “to the subject [i.e. you] was presented a red apple, at distance d, at coordinates x,y,z. By the physical laws of opticks and by the geometrical setting of the controlled lab we know that the sensor S of the retina of the left eye was stimulated by the light coming from the apple. We recorded a pattern of activity in the regions A, B, C of the brain, which we know from other knowledge (and statistical assumptions) that   A is busy with recognition of fruits, B is involved in contour recognition and C with memories from childhood.” I agree that is a completely bogus simplification of what the real eperimenter will say, but bear with me when I claim that the knowledge used by the experimenter for explaining how you see the apple has not much to do with the way you see and recognize the apple. In the course of the explanation, the experimenter used knowledge about the laws of optics, used measurements which are outside you, like coordinates and geometric settings in the lab, and even notions from the experimenter’s head, as “red”, “apple” and “contours”.

Should the experimenter not rely on physical laws? Or on geometrical asumptions (like the lab is in a piece of euclidean 3d space)? Of course he can rely on those. Because, in the case of physical laws, we recognize them as physical because they are invariant (i.e. change in a predictable way) on the observer. Because in the case of geometrical assumptions we recognize them as geometrical because they are invariant on the parametrization (which in the lab appears as the privilege of the observer).

But, as it is the case that optics can explain only what happens with the light until it hits the retina, not more, the assumptions in the head of the experimenter, even physical and geometrical, cannot be used as an explanation for the way you see. Because, simply put, it is much more likely that you don’t have a lab in the head which is in a euclidean space, with an apple, a lamp and rules for measuring distances and angles.

You may say that everybody knows that apples are not red, that’s a cheap shot because apples scatter light of all frequencies and it just happen that our sensors from the retina are more sensible at some frequencies than other. Obvious. However, it seems that not many recognize that contours are as immaterial as colors, they are in the mind, not in reality, as Koenderink writes in  Theory of “Edge-Detection”  JJ Koenderink – Analysis for Science, Engineering and Beyond, 2012.

The explanation of vision which uses an exterior homunculus becomes infinite regression unless we also explain how the exterior homunculus thinks about all these exterior facts as lab coordinates, rational deductions from laws of physics and so on. It is outrageous, but there is no other way.

Let’s forget about experiments on you and let’s think about experiments on fly vision. Any explanation of fly vision which uses knowledge which is not, somehow, embodied in the fly brain, falls into the (exterior)  homunculus fallacy.

So what can be done, instead? Should we rely on magic, or say that no explanation is possible, because any explanation will be issued by an exterior homunculus? Of course not. When studying vision, nobody in the right mind doubts about the laws of optics. They are science (i.e. reproducible and falsifiable). But they don’t explain all the vision, only the first, physical step. Likewise, we should strive for giving explanations of vision which are scientific, but which do not make appeal to a ghost, in the machine or outside the machine.

Up to now, I think that is the best justification for the efforts of understanding space not in a passive way.

Ancient Turing machine helps Chemical concrete machine

In this post I want to prove that only with arrow molecules and enzyme moves we can generate all we need for the chemical concrete machine, by using the minimal (?) formalism from  Chemical concrete machine, short list of gates and moves . This is done with the help of ideas from the old post   Ancient Turing machines (I): the three Moirai, which   contains a discussion about generating moves for graphic lambda calculus.

In the post Local FAN-IN eliminates GLOBAL FAN-OUT (II) , at the end, I prove a switch move from CO-COMM and FAN-IN moves. In the next figure, I call this move (which is, recall, a “macro move”, made by a succession of three moves) SWITCH.  In the same figure appears a move CREA’. The name is justified by the resemblance with the CREA move proposed in Ancient Turing machines (I): the three Moirai .

recrea_2

Before giving the proof of CREA’, let me remark that from these macro moves, together with the ones selected in the post  Chemical concrete machine, short list of gates and moves , we can recover all the generating moves of the three Moirai described in the ancient Turing machine post. For example, CREA’ , used for a triple of arrows, gives both the  Clotho’s  CREA  original move and the Atropos  GARB move.

Here is the proof of CREA’:

recrea_1

From a bag of arrows at our discretion, this move gives us fan-out gates and termination gates.  Moreover, it is pleasant to see that, as the SWITCH move is a consequence of CO-COMM and FAN-IN, the CREA’  move is a consequence of CO-ASSOC and FAN-IN.

We need the other three trivalent gates, let’s see how we can obtain them (generate them from arrows, by using moves, recall that moves are enzymes, in the world of the chemical concrete machine).

The fan-in gate is generated like this (we already generated termination gates):

recrea_3

The lambda abstraction and application gates can be generated by using the graphic beta move and a SWITCH move.

recrea_4

Now we have all the gates we need and we can proceed to constructing whatever combinator we want from them, mainly by using SWITCH moves. One elegant way for doing this would be to construct zippers first, by using the graphic beta moves, then construct the S,K,I combinators from zippers, as indicated in  Combinators and zippers.

Probably, the real chemical concrete machine will function with terms (molecules) created by some more natural chemistry tricks, but it is pleasant to see that in principle we can also construct what we need, before passing to the “computation” part, only by using arrows and moves.

Another discussion about computing with space

Computing with space vs space computing.

Space (real space, we all share) is not made of points. A point is an abstraction, the unattainable goal of a thought experiment, an atom of thought. Or a string of numbers (when we think with coordinates). Quantum physics tells us we can’t perform, even in principle, a physical experiment with the goal of exactly localizing the position of an object in space.

That’s pop philosophy. It might even be wrong (for example what quantum physics tells us is that we can’t perform physical experiments for localizing a particle in the phase space (position, momentum), not in the real space, whatever that means.

That’s also the turf of theoretical physicists, there are several, with various degree of mathematical soundness, theories about the structure of space. I shall not go in this direction, further.

Instead, I want to make a case for a biology inspired point of view. I made it before, repeatedly, starting with More than discrete or continuous: a bird’s view,  but now I have a bit more tools to tackle it, and a bit of more patience to not hurry to conclusions.

So, if you prefer the red pill, then read this. Can you think about space in terms of what it does, not what it is? Can you describe space as seen by a fly, or by a toddler, or you need to stick to cartesian conventions and then fall into the trap of continuous vs discrete, and so on?

Think like this: you are a fly and you have 10^5 neurons and 10^7 synapses. You are very good at flying by using about 10-20 actuators, and you see really well because the most part of your brain is busy with that. Now, where in that brain and how exactly there is place for a  representation of an euclidean 3d space? Mind you that humans have very little idea about how flies brains are capable of doing this and also, with their huge brains and their fast computers (much more faster and much bigger than a fly’s brain) were not successful yet to make a robot with the same competences as a fly.  (They will make one, there is no magic involved, but the constraints are really hard: an autonomous fly which can survive with the energy consumption comparable with the one of a real fly, without computing or human exterior help, in a natural environment, for 24hrs, find food, avoid traps and eventually mate.)

So, after this motivating example, I state my hypothesis: whatever space is (and that’s a very hard and old problem), let’s not think about it passively (like a robot fly which is driven by some algorithms which use advanced human knowledge about euclidean geometry, systems of coordinates and the laws of mechanics) as being a receptacle, a something. Let’s think about space as described by what you can do in it.

The fly, for example, cannot possibly have a passive representation of space (and for us is the same) in the brain, but it does have the possibility to manipulate it’s actuators as a function of what it sees (i.e. of what it’s receptors perceive and send further to the brain) and of the state of it’s brain (and maybe on the history of that state, i.e. memory, stored in a mysterious way in the same tiny brain).  However, actuators, sensors, brain and the environment are just one system, there is no ghost in, or outside that fly machine.

My hypothesis is that for the fly, that’s space.  For us is the same, but we are far more complex than the fly. However, deep in our brains there are “patterns” (are they assemblies of neurons, are they patterns of synaptic activity, is it chemical, electric, …?) which are very basic (a child learns to see in the first months) and which are space, for us.

Now I’ll get mathematical. There are spaces everywhere in math, for example when we say: that’s a vector space, that’s a manifold, or even this is a group, a category, and so on. We say like this, but what we actually have (in the mind) is not a manifold, or a vector space, a group or category, but some short collections of patterns (rules, moves, axioms) which can be applied to the said objects. And that is enough for doing mathematics. This can be formalized, for example it’s enough to have some simple rules involving gates with two inputs and an output (the dilations) and we can prove that these simple rules describe all the concepts associated to any vector space, for example. and moreover not using at any moment any external knowledge. A dilation is simply the pattern of activities related to  map making.

So, according to my hypothesis, a generic vector space is this collection of rules. When it comes to the dimension of it, there are supplementary relations to be added for being able to say that we speak about a 3d vector space, but it will be always about a generic 3d vector space. There is no concrete 3d space, when we say, for example, that we live in a 3d space, what we really say is that some of the things we can do in a generic 3d space can also be done in reality (i.e. we can perform experiments showing this, although there are again studies showing that our brain is almost as bad as concerns perceiving relations in the real space which correspond to theorems in geometry, as it is when we force it to do logic reasonings).

Conclusion for this part: there may be or not a passive space, the important thing is that when we think with space and about space, what we really do is using a collection of primitive patterns of thought about it.

Now, going to the Turing machine, it lacks space. Space can be used for enumeration, for example, but the Turing machine avoids this by supposing there is a tape (ordered set). It is proved that enumeration (i.e. the thing which resembles the most with space in the world of Turing machines) does not matter in the sense that it can be arbitrarily changed and still the conclusions and definitions from the field do not change. This is alike saying that the Turing machine is a geometrical object. But there is no geometrical description of a Turing machine (as far as I know) which is not using enumeration. This is alike saying that CS people can understand the concept of a sphere in terms of atlases , parametrizations and changes between those, but they can’t define spheres without them. Geometers can, and for this reason they can speak about what is intrinsically geometric about the sphere and what is only an artifact of the chosen coordinate system. In this sense, geometers are like flies: they know what can be done on a sphere without needing to know anything about coordinate systems.

Chemical concrete machine, short list of gates and moves

Continuing from  Local FAN-IN eliminates GLOBAL FAN-OUT (II)  and   Local FAN-IN eliminates global FAN-OUT (I)  I propose the following list of gates and moves, which are taken from graphic lambda calculus, with the FAN-IN and DIST moves added. (So this could be called the “chemical concrete machine sector”, and it has Turing universality, via the fact that it contains the combinatory logic.)

_______________

Principle: each gate is a molecule, each move is an enzyme.

_______________

Dictionary:  we need four trivalent gates, which can be distinguished one from another by looking at the number of inputs/outputs and from a choice of colour between red and green. To these add the arrows, loops and the termination gate. The translation from graphic lambda calculus notation to this new coloured notation is the following.

convention_1

Each of these gates is a molecule.  [Speculations: (1) by looking at DNA backbones, could be the 5′-3′ phosphate-deoxyribose backbone be used as \rightarrow? ]

_______________

The moves, translated. Each move is made possible by an enzyme (hence move = enzyme). Here is the list, with the names taken from graphic lambda calculus, by using the dictionary for translation.

convention_2

convention_3

convention_6

convention_4

  • Elimination of loops:

convention_5

_______________

Implementation needed, but this is out of my competence.  I was thinking that could be possible by using DNA origami techniques, but it might turn out that the schema here is even more close to the structure of DNA. This is an outrageous speculation, but I can’t stop to remark that there are 4 trivalent gates, making two pairs of duality (seen in the graphic beta move and in the FAN-IN move), and this resembles to the A-T, G-C pairing (but it might be a coincidence).

Chemical concrete machine not algorithmic self-assembly of DNA, nor Turing machine

This post is partially a request for references, partially a place for possible discussions, in the comments, and partially a place for clarifications of the previous post Why build a chemical concrete machine, and how? .

I started to read Erik Winfree’s thesis Algorithmic self-assembly of DNA and, to my joy, I see that at least at the knowledge level of 1998, what I propose is different. Here is a short brief of what I got from Winfree’s thesis  (along with my excuses for not representing things correctly and for misattributions) :

  • Adleman, Lipton, propose a model of DNA computing which uses exponential space, i.e. all the candidates for a solution of a combinatorial problem, each one represented by a strand of DNA, which are then filtered by a sequence of physical, or chemical manipulations of test tubes, of one of the types: separate (a tube into two others, according to the value of the variable at “i” position), merge (two tubes into one), detect. Variants (not due to Adleman, Lipton, Karp) are to add an amplify operation (like a FAN-OUT with variables) which transform a tube into two copies of it. Or (Boneh), instead of amplify, an append operation which adds a new variable with a give value.  All these models have variables and are based on the mechanism of selection of the solution from an exponential sea of candidates.
  • Hagiya, single-strand DNA computation, using a mechanism called “whiplash PCR”, which I don’t understand yet, but which has the computational power of a GOTO program. Has variables.
  • Winfree, single-strand DNA computation, but in 2D, where he proposes a “materialization” of a block cellular automaton (BCA) which has Turing universality. Has variables, tries to make a Turing machine.

_________________

In the post   Why build a chemical concrete machine, and how?   I mentioned Turing machines, but this is obviously wrong, as can be seen by looking at the previous post A chemical concrete machine for lambda calculus. I don’t want to have a ” syringe with 10^9 nano geometrical turing machines”, no, that’s misleading, what I call a chemical concrete machine works with lambda calculus terms (among other graphs, more geometrical, from graphic lambda calculus), which are reduced by chemical reactions (using for example the graphic beta move enzyme). That’s different.

_________________

At page 29 of Winfree’s thesis, there’s a picture illustrating various reactions and results of reactions between DNA strands.  I find interesting the Holliday junction, (image taken from the wiki page)

472px-Holliday_Junctionbecause it’s relation to crossings in knot diagrams. Recall that in the \lambda-TANGLE sector of the graphic lambda calculus, the graphic beta move appears as a SPLICE move.

Compare with these images from 3D crossings in graphic lambda calculus:

3d_crossing_3

3d_crossing_2

_________________

 

As that’s an exploratory post, kind of note to self, but open to anybody, take a look at this short course

[Updates will follow here]

Local FAN-IN eliminates GLOBAL FAN-OUT (II)

As I wrote in   Local FAN-IN eliminates global FAN-OUT (I) , the introduction of the three moves (FAN-IN and the two DIST moves) eliminates global FAN-OUT from the lambda calculus sector of the graphic lambda calculus.  In this post we shall see that we can safely eliminate other two moves, namely R1a, R1b, as well as improving the behaviour of the crossings from the \lambda-TANGLE sector.

The equilibrium is thus established: three new moves instead of the three old moves. And there are some unexpected advantages.

______________

Proposition.

Proof.  (a) Done in the following picture.

frob_6

The proof of (b) is here:

frob_7

Finally, here is the proof of (c):

frob_8

______________

The \lambda-TANGLE sector of the graphic lambda calculus is obtained by using the lambda-crossing macros

frob_9

In Theorem 6.3   arXiv:1305.5786 [cs.LO]  I proved that all the oriented Reidemeister moves (with the crossings replaced by the respective macros), with the exception of the moves R2c, R2d, R3a and R3h, can be proved by using the graphic beta move and elimination of loops.  We can improve the theorem in the following way.

Theorem.  By using the graphic beta move, elimination of loops, FAN-IN and CO-COMM, we can prove all the 16 oriented Reidemeister moves.

Proof. The missing moves R2c, R2d, R3a and R3h are all equivalent (by using the graphic beta move and elimination of loops, see this question/answer at mathoverflow) with the following switching move, which we can prove with FAN-IN and CO-COMM:

frob_2

The proof is done.

Local FAN-IN eliminates global FAN-OUT (I)

For being able to build  a chemical concrete machine (see the posts  A chemical concrete machine for lambda calculus  and  Why build a chemical concrete machine, and how?) we have to prove that  universal computation can be attained with only local moves in graphic lambda calculus. Or, the lambda calculus sector of the graphic lambda calculus, which gives universality to graphic lambda calculus, uses the global FAN-OUT move (see theorem 3.1 (d)  arXiv:1305.5786 [cs.LO]. Similarly, we see in proposition 3.2 (d), which describes the way combinatory logic appears in graphic lambda calculus, that again global FAN-OUT is used.

I want to describe a way to eliminate the global FAN-OUT move from combinatory logic (as appears in graphic lambda calculus via the algorithm described here ).

________________

There are reasons to dislike global moves in relation to B-type neural networks (see the last post    Pair of synapses, one controlling the other (B-type NN part III) ). Similar concerns can be found in the series of posts which has as the most recent one Dictionary from emergent algebra to graphic lambda calculus (III) .

In this first post I shall introduce a local FAN-IN move and two distributivity moves and I shall prove that they eliminate the need for using global FAN-OUT in combinatory logic. In the next post I shall prove that we can eliminate two other moves (so that the total number of moves of graphic lambda calculus stays the same as before) and moreover we can recover from distributivity and local FAN-OUT moves the missing oriented Reidemeister moves from the \lambda-TANGLE sector.

________________

Definition. The local FAN-IN move is described in the next figure and it can be applied for any \varepsilon \not = 1.

frob_1

Comments:

  • as you see, in the move appears a dilation gate, what can this has to do with combinatory logic? As I explained previously, the properties of the gates are coming through the moves they are involved in, and not from their name. I could have introduced a new gate, with two inputs and one output, call this new gate “fan-in gate” and use it in the FAN-IN move. However, wait until the next post to see that there are other advantages, besides the economy of gates available, in using a dilation gate as a fan-in.
  • the FAN-IN move resembles to the packing arrows trick which is used extensively in the neural networks posts.  This suggests to use as a  fan-in gate

synapse_1

the green triangle gate and as fan-out gate the red triangle gate. This would eliminate the \Upsilon gate from the formalism, but is not clear to me how this replacement would interfere with the rest of the moves.

  • the FAN-IN move resembles with the dual of the graphic beta move, but is not the same (recall that until now I have not accepted the dual of the graphic beta move in the list of the moves of graphic lambda calculus, although there are strong reasons to do so):

dist_2

dist_1

which is needed in the emergent algebra sector in order to make the dictionary to work (and related as well to the goal of non using global FAN-OUT in that sector).  This latter move is in fact a distributivity move (see further), but we are free to choose different moves in different sectors of the graphic lambda calculus,

  • I know it is surprising that until now there was nothing about evaluation strategies in graphic lambda calculus, the reason being that because there are no variables then there is noting to evaluate. However, the situation is not so simple, at some point, for example in the chemical concrete machine or for neural networks, some criterion for choosing the order of moves will be needed. But it is an important point to notice that replacing global FAN-OUT (which could be seen as a remnant of having variables and evaluating them) by local FAN-IN has nothing to to with evaluation strategies.

________________

Definition: The distributivity moves (related to the application and lambda abstraction gates) are the following:

dist_3

dist_4

Comments:

  • the first distributivity move is straighforward, an application gate is just doubled and two fan-out moves establish the right connections. We see here why the “mystery move” can be seen as a kind of distributivity move,
  • the second distributivity move is where we need a fan-in gate (and where we use a dilation gate instead): because of th orientation of the arrows, after we double the lambda abstraction gates, we need to collect two arrows into one!

________________

Combinatory logic terms appear in graphic lambda calculus as  trees made by application gates, with leaves one of the combinators S, K, I (seen as graphs in GRAPH.  I want to show the following. [UPDATE: made some corrections.]

Theorem.   We can replace the global FAN-OUT move with a sequence of local FAN-IN ,  DIST, CO-COMM and local pruning moves, every time the global FAN-OUT move is applied to a term made by SKI combinators.

Proof.  First, remark that a sequence of  DIST moves for the application gate allows to reduce the problem of replacing global FAN-OUT moves for any combinator to the problem of replacing it for S, K, and I. This is because the DIST move for the application gate allows to do the FAN-OUT of trees of application gates:

dist_5

Now we have to prove that we can perform global FAN-OUT for I , K, S combinators.  For the combinator I the proof is the following:

frob_4

For the combinator K we shall also use a local pruning move:

frob_5

Finally, the proof for the combinator S is the following:

frob_3

Now we are going to use 3 DIST moves, followed by the switch of arrows explained in   Local FAN-IN eliminates GLOBAL FAN-OUT (II) , which is applied in the dashed green ellipse from the next figure:

frob_10

And we are done.

UPDATE: At close inspection, it turns out that we don’t need to do switch (macro) moves. Instead, if we go back at the point we were before the last figure,  we may use  first CO-ASSOC and then perform the three FAN-IN moves .

new_analysis

I remembered that I tried once before to keep a blog, in 2005. It still exists, but links are broken now. What a fool for not insisting with it! I reproduce further the one post, with the broken links retrieved from the Wayback Machine.

New analysis

The purpose of this blog is to report on the evolution of some very hot new ideas in mathematics and physics. The new emerging trend seems to be oriented toward a new analysis, in the same way as new geometries, other than euclidean, emerged 2 centuries ago.

This is a rather “technical” blog, intended for mathematicians and physicists. If you are looking for new ideas, hope this will be a nice place to find some of them. If you are the “publish rubbish or perish” kind of person, this place is not of much use for you. But if you are the “think before you publish” guy, and if you have a rather large horizont of mathematical interests, then you are welcome here.

The languages which I shall use are (mostly broken) English and sometimes Romanian. I reserve the right to express here also some of my personal opinions, remotely related to the main subject of the blog.

Here it starts.

For the moment the most relevant page to look for is:

http://irmi.epfl.ch/cag/buliga_sr.html

but use the mighty google to search for papers on the web, with the keywords: subriemannian , “sub-Riemannian” , “Carnot-Caratheodory”, “curvature metric space”

check also the wonderful http://www.arxiv.org . My papers from there are available at

http://www.arxiv.org/find/math/1/au:+Buliga_M/0/1/0/all/0/1

The weekend round table on UD

The comments from   Is 3D Cube a predecessor of UD?   were very useful as concerns the  idea of making open source UD like programs. There are already two projects, created by Bauke and Jim respectively, see the page   UD projects here for the relevant links.

We cannot possibly know if any of  these two proposals is like Bruce Dell’s UD, but it does not matter much because Bauke and Jim programs may well turn out to be as good as Dell’s, or why not even better. (However,  I still want to know how exactly the database of Saj’s 3D Cube is done, because of the claims he made many years ago, which are almost identical with the ones made by Dell, see the link from the beginning of this post.)

Enough chit-chat, the reason for this post   is that I suppose new discussions will follow, for example about Bauke’s still pending detailed explanations about his program. Also, any day now Jim might amaze us.

So, please start commenting here instead, if you feel the need to ask or answer questions about the two projects. Or, hey, you are welcome to announce yours!

_________

UPDATE:   I add here Bauke’s explanations from this comment :

My algorithm consists of three steps:
1. Preparing the quadtree. (This process is very similar to frustum culling, I’m basically culling all the leaf nodes of the quadtree that are not in the frustum.) This step can be omitted at the cost of higher CPU usage.
2. Computing the cubemap. (Or actually only the visible part, if you did step 1.) It uses the quadtree to do occlusion culling. The quadtree is basically an improved z-buffer, though since the octree is descended in front to back order, it is sufficient to only store ‘rendered’ or ‘not rendered’. It furthermore allows to do constant time depth checks for larger regions.

3. Rendering the cubemap. This is just the common cubemap rendering method. I do nothing new here.

My description only explains step 2, as this is the core of my algorithm. Step 1 is an optimization and step 3 is so common that I expect the reader to already know how this step works.

Step 2 does not use the display frustum at all. It does do the perspective. But does so by computing the nearest octree leaf (for the sake of simplicity I’m ignoring the LOD/mipmap/anti-aliassing here) in the intersection of a cone and the octree model. This is shown in the following 2D images:

sbus

 
[image source]

 

sn29
[image source]

I don’t know what you mean with ‘scaling of each pixel’, but I believe that scaling of pixels only happens in step 3. In step 1 and 2 I completely ignore that this happens.

I hope this answers your questions. If not, please tell which of the 3 steps you do not understand.

 

 

_________

UPDATE: You may like techniques used here  [source]

Why build a chemical concrete machine, and how?

Asked about this, I spent a bit thinking and I arrived at  this very brute answer:

What I have in mind can be split in two.

There is a first part concerning graphic lambda calculus seen as it’s about real molecules interacting in space. For this part I would like to construct graphs which are like a Turing machine (or other computing devices) then, the important step is to eliminate everything which is due to our human 1d writing conventions (see details in the rant from the first part of this post) and thinking and simplify such “machines”, in the same way, for example, like I saw it’s possible

A serious tool for doing this would be, for example, a program which allows to visualize and perform moves  (automatically and from the keyboard) on graphs in GRAPH.

The second part, which goes in parallel, would be to try to find in the real world (here DNA origami looks like a possible tool) ways to construct chemically, physically, such machines, which are naturally adapted to the real world because they are geometrical (an object or a property is geometrical if it can be described, or it’s invariant to an arbitrary change of parametrization, in our case, an arbitrary renaming).  For this part I want to understand first how DNA origami works, to the level of being able to absorb information and understand some of the hurdles.  This leads to applications, which are still vague in my head, but I was impressed by this video

as well as by research of Jean-Pierre Banatre and Daniel Le Metayer.

In conclusion, I can’t imagine what a syringe with 10^9 nano geometrical turing machines  graphs representing lambda terms [see UPDATE]  can’t do.

______________

UPDATE:  Corrections to this post are made in  Chemical concrete machine not algorithmic self-assembly of DNA, nor Turing machine   , where it is stressed that the “chemical concrete machine”, even if it has Turing universality property, it is not intended to be a Turing machine, (nor an automaton), as is wrongly suggested in this post.

Academic Spring and OA movement just a symptom, not cause of change

… a reaction to profound changes which  question the role of universities and scholars. It’s a symptom of an adaptation attempt.

The OA movement, which advances so slowly because of the resistance of the scholars (voluntarily lulled by the propaganda machine of the association between legacy publishing industry and rulers of universities), is just an opening for asking more unsettling questions:

  • is the  research article as we know it a viable vehicle of communication?
  • what is the difference between peer-reviewing articles and writing them?
  • should review be confined to scholars, or informed netizens (for example those detecting plagiarism) have their place in the review system?
  • is an article a definite piece of research, from the moment of publishing it (in whatever form, legacy or open), or it is forever an evolving project, due to contributions from a community of interested peoples, and if the latter is the case, then who is the author of it?
  • is it fair to publish an article inspired (in the moral sense, not the legal one) from information freely shared on the net, without acknowledging it, because is not in the form of an article?
  • is an article the goal of the research, as is the test the goal of studying?

Which is our place, as researchers? Are we like the scholars of medieval universities, becoming increasingly irrelevant, less and less creative, with our modern version of rhetoric and theological studies, called now problem solving and grant projects writing?

If you look at the timing of the end of the medieval universities and the flourishing of the early modern ones, there are some patterns.We see that (wiki source on early modern universities):

At the end of the Middle Ages, about 400 years after the first university was founded, there were twenty-nine universities spread throughout Europe. In the 15th century, twenty-eight new ones were created, with another eighteen added between 1500 and 1625.[33] This pace continued until by the end of the 18th century there were approximately 143 universities in Europe and Eastern Europe, with the highest concentrations in the German Empire (34), Italian countries (26), France (25), and Spain (23) – this was close to a 500% increase over the number of universities toward the end of the Middle Ages.

Compare with the global spread of the printing press. Compare with the influence of the printing press on the Italian Renaissance (read about Demetrios Chalkokondyles).

The scientific revolution is

Traditionally held to have begun in 1543, when were first printed the books De humani corporis fabrica (On the Workings of the Human Body) by Andreas Vesalius, which gave a new confidence to the role of dissection, observation, and mechanistic view of anatomy,[59] and also De Revolutionibus, by Nicolaus Copernicus. [wiki quote]

Meanwhile, medieval universities faced more and more problems, like [source]

Internal strife within the universities themselves, such as student brawling and absentee professors, acted to destabilize these institutions as well. Universities were also reluctant to give up older curricula, and the continued reliance on the works of Aristotle defied contemporary advancements in science and the arts.[36] This era was also affected by the rise of the nation-state. As universities increasingly came under state control, or formed under the auspices of the state, the faculty governance model (begun by the University of Paris) became more and more prominent. Although the older student-controlled universities still existed, they slowly started to move toward this structural organization. Control of universities still tended to be independent, although university leadership was increasingly appointed by the state.[37]

To finish with a quote from the same wiki source:

The epistemological tensions between scientists and universities were also heightened by the economic realities of research during this time, as individual scientists, associations and universities were vying for limited resources. There was also competition from the formation of new colleges funded by private benefactors and designed to provide free education to the public, or established by local governments to provide a knowledge hungry populace with an alternative to traditional universities.[53] Even when universities supported new scientific endeavors, and the university provided foundational training and authority for the research and conclusions, they could not compete with the resources available through private benefactors.[54]

So, just a symptom.

______________

UPDATE:  Robin Osborne’s article is a perfect illustration  of the confusion which reigns in academia. The opinions of the author, like the following one [boldfaced by me]

When I propose to a research council or similar body that I will investigate a set of research questions in relation to a particular set of data, the research council decides whether those are good questions to apply to that dataset, and in the period during which I am funded by that research council, I investigate those questions, so that at the end of the research I can produce my answers.

show more than enough that today’s university is medieval university reloaded.  How can anybody decide a priori which questions will turn out to be good, a posteriori?  Where is the independence of the researcher? How is it possible to think that a research council may have any other than a mediocre glimpse into the eventual value of a line of research, based on bureaucratic past evidence? And for a reason: because research is supposed to be an exploration, a creation of a new territory, it’s not done yet at the moment of grant application. (Well, that’s something everybody knows, but nevertheless we pretend it does not matter, isn’t it sick?)  Instead, conformity reigns.  Mike Taylor spends a post on this article, exposing it’s weakness  as concerns OA.

______________

UPDATE 2: Christopher Lee takes the view somewhat opposite to the one from this post, here:

In cultured cities, they formed clubs for the same purpose; at club meetings, particularly juicy letters might be read out in their entirety. Everything was informal (bureaucracy to-science ratio around zero), individual (each person spoke only for themselves, and made up their own mind), and direct (when Pierre wrote to Johan, or Nikolai to Karl, no one yelled “Stop! It has not yet been blessed by a Journal!”).

To use my nomenclature, it was a selected-papers network. And it worked brilliantly for hundreds of years, despite wars, plagues and severe network latency (ping times of 109 msec).

Even work we consider “modern” was conducted this way, almost to the twentieth century: for example, Darwin’s work on evolution by natural selection was “published” in 1858, by his friends arranging a reading of it at a meeting of the Linnean Society. From this point of view, it’s the current journal system that’s a historical anomaly, and a very recent one at that.

 

I am very curious about what Christopher Lee will tell us about solutions to  escape  wall-gardens and I wholeheartedly support the Selected Papers Net.

But in defense of my opinion that the main problem resides in the fact that actual academia is the medieval university reloaded, this  quote (taken out out context?) is an example of the survivorship bias. I think that the historical anomaly is not the dissemination of knowledge by using the most efficient technology, but sticking to old ways when revolutionary new possibilities appear. (In the past it was the journal and at that time scholars cried “Stop! it is published before being blessed by our authority!”, exactly alike scholars from today who cry against OA. Of course, we know almost nothing today about these medieval scholars which formed the majority at that time, proving again that history has a way to punish stupid choices.)

Pair of synapses, one controlling the other (B-type NN part III)

In  Teaser (I) and Teaser (II) I discussed about the possibility to construct neural networks with controlled connections, resembling the B-type neural networks of Turing, in the following sense:

  • they are formed by “neurons”, which in Turing’s description are boolean logic gates, and here they are graphs from graphic lambda calculus which correspond to lambda calculus terms. But there’s no real restriction on that, graphic lambda calculus being more general and powerful than untyped lambda calculus, one may consider “neurons” which are outside the lambda calculus sector. The fact is that a “neuron” here should be interpreted as any graph in GRAPH with at least one input but only one output. Later, when we shall speak about the order of performing moves in such neural networks, it will be seen that each “neuron” is a bag containing graphs which are modified (or reduced, as people say) independently one from another. Neurons are packing conventions from a formal viewpoint, but this viewpoint may not be the best one, a better viewpoint would be to see neurons as well as synapses, described further, as real constructs, like in the related project of the chemical concrete machine, In particular, neurons may do other things than computing in the lambda calculus sense (classical computation), they may do some more geometrical or physical or robot-like activities, not usually considered computations.
  • the connections between neurons are controlled in a B-type NN, by TRUE – FALSE control wires or inputs. Turing explains that controls themselves can be realized as constructions with boolean neurons (i.e. in his language B-type NNs are particular cases of his A-type NNs). In Teaser (II)    is explained how the connections between graphic lambda calculus neurons can be constructed by using a 2-zipper and a switch, such that a connection is controlled (by the same TRUE-FALSE mechanism, but this time TRUE and FALSE are literary the graphs of the corresponding terms in lambda calculus) by another connection.

There is an important difference, besides the power of the calculi used (boolean vs. graphic lambda), namely that there is no signal transmitted through the wires  of the network. That is because graphic lambda calculus does not need variable names. I shall come back to this very important point (which is a big advantage for implementing such networks in reality) in a future post, but let me mention two simple facts:

  • an analogy: think about hardware and software, a difference between them is that software may be associated to the pattern of signals which circulates in the hardware. The hardware is the machine inhabited by the software, they are not in the same plane of physical reality, right? Well, in this implementation there is literary no difference between those, exactly because there are no signals (software) transmitted through the hardware.
  • this use of names, software, variable and signals is a pain for those trying to make sense how to implement in reality computing constructs. But, if you think, the need to name that stuff “x” and that other stuff “y”, to make alpha conversions in order to avoid name clashes, or even to describe computations in a linear string of symbols, all this are in no relation with physical reality, they are only cultural constraints of humans (and their artificial constructs). It all has to do with the fact that humans communicate science through writing, and of course, it has to do with the enumeration techniques of the cartesian method , which “is designed as a technique for understanding performed by one mind in isolation, severely handicapped by the bad capacity of communication with other isolated minds”. Molecules in a cell or in a solution do not align in a string according to the experimenter writing habits, from left to right, right to left, or vertical. They just don’t wait for an external  clock  to rhythm their ballet, nor for the experimenter to draw it’s familiar coordinate system. These are all limitations of the method, not of nature.

_____________

Further, in this post, I shall use “synapse” instead “connection”.  Let’s see, now that we know we can implement synapses by a TRUE-FALSE mechanism, let’s see if we can do it simpler. Also, if it is possible to make the “synapses control other synapses” concept more symmetric.

Instead of the little magenta triangles used in Teaser I, II posts (which are not the same as the magenta triangles used in the chemical concrete machine post) , I shall use a more clear notation:

synapse_1

The design of a synapse proposed in Teaser (II)  involved two switches and a zipper, justified by the direct translation of TRUE-FALSE lambda calculus terms in the freedom sector of graphic lambda calculus.  Think now that in fact we have there two synapses, one controlling the other. The zipper between them is only a form of binding them together in unambiguous way.

A simplified form of this idea of a pair of synapses is the following:

synapse_2

In the upper part of the figure you see the pair of synapses, taking the a form like  this: ><. There are two synapses there, one is >, which is controlled by the other <.  The connection between the blue 1 and the red 3′ is controlled by the other synapse. In order to see how, let’s perform a graphic beta move and obtain the graph from the lower part of the figure.

The red 3 can connect, in the sense explained by the switch mechanism from Teaser (II) with blue 1′ or blue 2′, all three of them belonging to the controlling synapse. Say red 3  connects with blue 1′ and look what happens:

synapse_3

OK, so we obtain a connection between blue 1 and red 3′. Suppose now that red 3 connects with blue 2′, look:

synapse_4

Yes, blue 1 does not connect with red 3′, they just exchanged a pair of termination gates. That’s how the pair of synapses work.

Finally, let me remark that the use of termination gates is a bit ugly, it breaks the symmetry,  is not really needed.

A chemical concrete machine for lambda calculus

UPDATE: See the Chemical concrete machine article (2013), followed by Molecular computers (2015), (arXiv 2018).

This post is a call for building one. More precisely, for building one for graphic lambda calculus, which satisfies all the requests  from section 5 of The chemical abstract machine by Berry and Boudol  for a calculus more general than lambda calculus.

First a short comment on “The chemical abstract machine”. This is a very interesting article, thanks are due  to Mike Stay for telling me about it, in relation to what is described in my previous post  Can graphic lambda calculus be implemented in some form of DNA computing?  The following quote describes exactly what I was looking for (p.1 from the linked file):

Intuitively, the state of a system is like a chemical solution in which floating molecules can interact with each other according to reaction rules … Solutions are finite multisets of molecules …

But from here I will not jump to identify terms in lambda calculus with solutions, like it’s done in section 5.2 on the \gamma calculus. Nor shall I use membranes and airlocks, as useful as they seem (and probably there is a possible implementation of those in graphic lambda calculus).

Finally, there is something which I don’t understand in the article, concerning variables and substitutions. I might be wrong, please tell if so, but apparently an abstract chemical machine still uses names for variables, which appear as “ions”. What I don’t get is: how are two solutions, each one corresponding to a lambda term, the same if the two lambda terms are the same by renaming the variables?

In graphic lambda calculus there is no such problem, because there are no variable names. Moreover, lambda terms (which appear as certain graphs in graphic lambda calculus) are molecules and the moves between them (like the graphic beta move) are chemical reactions.

How can this be done? Here is sketch, mind you that I propose things which I believe are possible from a chemical perspective, but I don’t have any chemistry knowledge.  If you do, and if you are interested to make a chemical concrete machine for graphic lambda calculus, then please contact me.

The graphs from GRAPH which we need for the lambda calculus sector of the graphic lambda calculus, are  made by three gates corresponding to lambda abstraction, application and fan-out (I shall ignore the termination gate). So we need three molecules.

enzyme_1

The five coloured triangles are parts of the molecules which bond one with the other. Say there is a bond strength associated with each pairing, such that the bigger is the bond strength, more easily the bond is made and more stronger it is.

Remark that the molecules from the right of the figure don’t seem to have oriented arrows inside. That is why we need several types of bonding places. The table of bond strengths is this:

enzyme_3

The strength coefficients are taken out of … my imagination, such that they satisfy a number of mental experiments I did with them.  As you see there are:

  • two bonding places — yellow and red, which correspond to input half-arrows,
  • two bonding places — blue and green, which correspond to output half-arrows,
  • one bonding place – magenta, which can be seen as well as input or output half-arrow.

The bipartite graph from the lower part of the figure shows which bonding places CAN bond (i.e. they have bonding strength not equal to 0).  In the upper part of the figure there is a table with strengths.

As you see, this solves the problem of representing decorated nodes of oriented graphs. Nice.

Now, let’s pass to the main move, the graphic beta move. This should be a chemical reaction. Imagine that we have an enzyme called “beta”, which recognizes magenta-magenta bonds. When it does recognize such a bond, it does like in the next figure:

enzyme_2

So, the beta enzyme cuts the other bonds, like in the middle part of the picture, then the bonding places bond according to the strength table. Voila!

If you want to play with, here is a “chemical” representation of the 2-zipper, try to see how it works.

enzyme_4

I hope this adds more details to my call of building a real, concrete chemical machine (or to recognize one with at least the expressivity of untyped lambda calculus). Can anybody do it?

Teaser: B-type neural networks in graphic lambda calculus (II)

The connections in a B-type neural network can be trained.  The following  quote and figure are taken from the article  Turing’s Neural Networks of 1948, by Jack Copeland and Diane Proudfoot:

Turing introduced a type of neural network that he called a ‘B-type unorganised machine’, consisting of artificial neurons, depicted below as circles, and connection-modifiers, depicted as boxes. A B-type machine may contain any number of neurons connected together in any pattern, but subject always to the restriction that each neuron-to-neuron connection passes through a connection-modifier.

connecmod

A connection-modifier has two training fibres (coloured green and red in the diagram). Applying a pulse to the green training fibre sets the box to pass its input–either 0 or 1–straight out again. This is pass mode. In pass mode, the box’s output is identical to its input. The effect of a pulse on the red fibre is to place the modifier in interrupt mode. In this mode, the output of the box is always 1, no matter what its input. While it is in interrupt mode, the modifier destroys all information attempting to pass along the connection to which it is attached. Once set, a connection-modifier will maintain its function unless it receives a pulse on the other training fibre. The presence of these modifiers enables a B-type unorganised machine to be trained, by means of what Turing called ‘appropriate interference, mimicking education’.

Let’s try to construct such a connection in graphic lambda calculus.  I shall use the notations from the previous post  Teaser: B-type neural networks in graphic lambda calculus (I).

3. Connections.   In lambda calculus, Church booleans are the following terms: TRUE = \lambda x . \lambda y .x and FALSE = \lambda x. \lambda y. y (remark that TRUE is the combinator K).  By using the algorithm for transforming lambda calculus terms into graphs in GRAPH, we obtain the following graphs:

switch_5

They act on other graphs (A, B) like this:

switch_6

The graphs are almost identical: they are both made by a 2-zipper with an additional termination gate and a wire. See the  post   Combinators and zippers  for more explanations about TRUE, or K.

I am going to exploit this structure in the construction of a connection. We are going to need the following ingredients: a 2-zipper, an INPUT BOX (otherwise called “switch”, see further) and an OUTPUT BOX,

which is almost identical with a switch (it is identical as a graph, but we are going to connect it with other graphs at each labelled edge):

switch_2

I start with the following description of objects and moves from the freedom sector of graphic lambda calculus (the magenta triangles were used also in the previous post).  I call the object from the middle of the picture a switch.

switch_1

As you can see, a switch can be transformed into one of the two graphs (up and down parts of the figure).  We can exploit the switch in relation with the TRUE and FALSE graphs. Indeed, look at the next figure, which describes graphs almost identical with the TRUE and FALSE graph (as represented by using zippers), with an added switch:

switch_4

Now we are ready for describing a connection like the one from the B-type neural networks (only that better, because it’s done in graphic lambda calculus, thus much more expressive than boolean expressions). Instead of training the connection by a boolean TRUE of FALSE input (coming by one of the green or red wires in the first figure of the post), we replace the connection by an OUTPUT BOX (should I call it “synapse”? I don’t know yet) which is controlled by a switch. The graph of a connection is the following:

switch_3

The connection between an axon and a dendrite is realized by having the axon at “1” and the dendrite at “3”. We may add a termination gate at “2”, but this is irrelevant somehow. At the top of the figure we have a switch, which can take any of the two positions corresponding, literary, to TRUE or FALSE. This will transform the OUTPUT BOX into one of the two possible graphs which can be obtained from a switch.

You may ask why did I not put directly a switch instead of an OUTPUT BOX. Because, in this way, the switch itself may be replaced by the OUTPUT BOX of another connection. The second reason is that by separating the graph of the connection into a switch, a 2-zipper and an OUTPUT BOX, I proved that what is making the switch to function is the TRUE-FALSE like input, in a rigorous way. Finally, I recall that in graphic lambda calculus the green dashed ovals are only visual aids, without intrinsic significance. By separating the OUTPUT BOX from the INPUT BOX (i.e. the switch) with a zipper, the graph has now an unambiguous structure.